Kongsberg Digital K-Sim Fishery Simulator Wins Award

Kongsberg Digital K-Sim Fishery Simulator Wins Award

Kongsberg Digital’s acclaimed K-Sim Fishery simulator training solution has secured an important industry accolade by winning the SAFETY4SEA 2020

Training Award. After being shortlisted back in July, K-Sim Fishery was voted into first place by a substantial number of instructors and students who have been impressed by this vessel-specific training resource. Tone-Merete Hansen, Sr. VP in Kongsberg Digital accepted the prize on behalf of Kongsberg Digital with a speech delivered by video link at a virtual awards ceremony which was streamed on YouTube, Facebook and LinkedIn on Tuesday, October 20th. It was noted that the K-Sim Fishery satisfied the deceptively simple but exacting award criteria by successfully representing a significant achievement, breakthrough or contribution in aspect[s] of maritime training.

SAFETY4SEA is widely regarded as an influential source of global shipping news with an unswerving emphasis on stories which promote and engender maritime safety, sustainability, regulatory compliance and pivotal developments in smart technology. The K-Sim Fishery simulator training solution exemplifies these values, providing trainees with a safe, environmentally-responsible and high-tech means of gaining all the operational skills and experience they will require when tackling their job roles on fishing boats in the real world.

In line with newly intensified STCW training requirements, the K-Sim Fishery simulator enables instructors to monitor and evaluate each exercise undertaken by individual users. These exercises can be repeated as many times as is necessary for trainees to attain the mandated standard of competence in operating onboard navigation and control equipment as well as gaining essential winch-handling expertise. By precisely replicating the instrumentation and working environment on the bridge of a fishing vessel in all weathers, sea states and situations, the simulator produces an immersive experience which is effectively indistinguishable from the real thing. The main difference, of course, is that users can learn from their mistakes without causing any hardship or suffering any consequence.

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